电动汽车在不确定的环境中运输危险物质的多目标路线优化
Qian Zhang1, Zejian Zhang2, Chao Ma3
1School of Automobile and Traffic Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China.
Scientific reports
|December 22, 2025
概括
这项研究优化了电动汽车运输危险物质的路线,考虑了安全性和效率. 改进的算法提高了路线规划,降低了风险和成本,同时提高了客户满意度.
科学领域:
- 运输工程 运输工程
- 物流管理物流管理
- 环境科学 环境科学
背景情况:
- 危险物质运输需要高度的安全性和及时性.
- 电动汽车 (EV) 为物流提供了一个可持续的替代方案.
- 人口密度和货物数量的不确定性会影响运输风险和电力消耗.
研究的目的:
- 为使用电动汽车运输9类危险物质开发一个多目标路径优化模型.
- 尽量减少运输风险和成本,同时最大限度地提高客户满意度.
- 为了解决人口密度和货物数量的不确定性.
主要方法:
- 开发了一个多目标路径优化模型,结合了事故概率,货物量和时间窗口的约束.
- 一个改进的非主导排序遗传算法II (NSGA-II),称为H-NSGA-II,通过将贪算法特征与传统的NSGA-II融合而设计.
- 进行了案例验证,以评估算法在获得高质量的帕雷托解决方案方面的效率.
主要成果:
- 该H-NSGA-II算法有效地产生高质量的帕雷托解决方案,用于危险物质运输.
- 与标准NSGA-II相比,H-NSGA-II取得了显著的改进:运输风险减少14.40%,运输成本减少12.81%,平均客户满意度增加13.53%.
- 该模型在复杂的城市物流场景中有效平衡竞争目标.
结论:
- 开发的H-NSGA-II算法为城市9类危险物质的安全,经济和绿色分配提供了有效的决策支持.
- 这项研究强调了电动汽车在提高危险品物流的可持续性和效率方面的潜力.
- 这些发现有助于优化在不确定的条件下对敏感货物的城市运输网络.
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